






"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[int]      =  ["""pixel_values"""]
					def __init__(							self							:	List[Any]     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Dict[str, int] = None     , UpperCAmelCase							:	float = None     , UpperCAmelCase							:	PILImageResampling = PILImageResampling.BILINEAR     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Union[int, float] = 1 / 255     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , **UpperCAmelCase							:	Dict     , )	->       None:
											super().__init__(**UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			size if size is not None else {'shortest_edge': 384}
											lowerCAmelCase   :Optional[Any]						=			get_size_dict(UpperCAmelCase     , default_to_square=UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			do_resize
											lowerCAmelCase   :Union[str, Any]						=			size
											# Default value set here for backwards compatibility where the value in config is None
											lowerCAmelCase   :Union[str, Any]						=			crop_pct if crop_pct is not None else 224 / 256
											lowerCAmelCase   :Optional[Any]						=			resample
											lowerCAmelCase   :Any						=			do_rescale
											lowerCAmelCase   :Any						=			rescale_factor
											lowerCAmelCase   :int						=			do_normalize
											lowerCAmelCase   :Any						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowerCAmelCase   :Optional[int]						=			image_std if image_std is not None else IMAGENET_STANDARD_STD
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Dict[str, int]     , UpperCAmelCase							:	float     , UpperCAmelCase							:	PILImageResampling = PILImageResampling.BICUBIC     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	str     , )	->       np.ndarray:
											lowerCAmelCase   :Optional[Any]						=			get_size_dict(UpperCAmelCase     , default_to_square=UpperCAmelCase   )
											if "shortest_edge" not in size:
																	raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""   )
											lowerCAmelCase   :int						=			size['shortest_edge']

											if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	lowerCAmelCase   :Union[str, Any]						=			int(shortest_edge / crop_pct   )
																	lowerCAmelCase   :Any						=			get_resize_output_image_size(UpperCAmelCase     , size=UpperCAmelCase     , default_to_square=UpperCAmelCase   )
																	lowerCAmelCase   :List[Any]						=			resize(image=UpperCAmelCase     , size=UpperCAmelCase     , resample=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=UpperCAmelCase     , size=(shortest_edge, shortest_edge)     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
											else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    UpperCAmelCase     , size=(shortest_edge, shortest_edge)     , resample=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Union[int, float]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	Tuple     , )	->       Optional[int]:
											return rescale(UpperCAmelCase     , scale=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Union[float, List[float]]     , UpperCAmelCase							:	Union[float, List[float]]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	Any     , )	->       np.ndarray:
											return normalize(UpperCAmelCase     , mean=UpperCAmelCase     , std=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	ImageInput     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	Dict[str, int] = None     , UpperCAmelCase							:	float = None     , UpperCAmelCase							:	PILImageResampling = None     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	float = None     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , UpperCAmelCase							:	Optional[Union[str, TensorType]] = None     , UpperCAmelCase							:	ChannelDimension = ChannelDimension.FIRST     , **UpperCAmelCase							:	Any     , )	->       PIL.Image.Image:
											lowerCAmelCase   :Optional[Any]						=			do_resize if do_resize is not None else self.do_resize
											lowerCAmelCase   :Optional[int]						=			crop_pct if crop_pct is not None else self.crop_pct
											lowerCAmelCase   :Union[str, Any]						=			resample if resample is not None else self.resample
											lowerCAmelCase   :Any						=			do_rescale if do_rescale is not None else self.do_rescale
											lowerCAmelCase   :str						=			rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCAmelCase   :str						=			do_normalize if do_normalize is not None else self.do_normalize
											lowerCAmelCase   :str						=			image_mean if image_mean is not None else self.image_mean
											lowerCAmelCase   :Optional[Any]						=			image_std if image_std is not None else self.image_std

											lowerCAmelCase   :Optional[Any]						=			size if size is not None else self.size
											lowerCAmelCase   :List[Any]						=			get_size_dict(UpperCAmelCase     , default_to_square=UpperCAmelCase   )

											lowerCAmelCase   :str						=			make_list_of_images(UpperCAmelCase   )

											if not valid_images(UpperCAmelCase   ):
																	raise ValueError(
																	    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																	    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

											if do_resize and size is None or resample is None:
																	raise ValueError('Size and resample must be specified if do_resize is True.'   )

											if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError('crop_pct must be specified if size < 384.'   )

											if do_rescale and rescale_factor is None:
																	raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

											if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

											# All transformations expect numpy arrays.
											lowerCAmelCase   :Tuple						=			[to_numpy_array(UpperCAmelCase   ) for image in images]

											if do_resize:
																	lowerCAmelCase   :Any						=			[self.resize(image=UpperCAmelCase     , size=UpperCAmelCase     , crop_pct=UpperCAmelCase     , resample=UpperCAmelCase   ) for image in images]

											if do_rescale:
																	lowerCAmelCase   :int						=			[self.rescale(image=UpperCAmelCase     , scale=UpperCAmelCase   ) for image in images]

											if do_normalize:
																	lowerCAmelCase   :Any						=			[self.normalize(image=UpperCAmelCase     , mean=UpperCAmelCase     , std=UpperCAmelCase   ) for image in images]

											lowerCAmelCase   :Any						=			[to_channel_dimension_format(UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											lowerCAmelCase   :Tuple						=			{'pixel_values': images}
											return BatchFeature(data=UpperCAmelCase     , tensor_type=UpperCAmelCase   )







"""simple docstring"""


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters() ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is False
																		), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is True
																		), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__=True ):





						'''simple docstring'''






						model.train()
						lowerCAmelCase   :Dict						=			model(a__ )
						lowerCAmelCase   :Union[str, Any]						=			F.mse_loss(a__     ,      target.to(output.device ) )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(a__ )


def        UpperCAmelCase      (					a__     ,      a__=False ):





						'''simple docstring'''






						set_seed(42 )
						lowerCAmelCase   :Any						=			RegressionModel()
						lowerCAmelCase   :List[str]						=			deepcopy(a__ )
						lowerCAmelCase   :str						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						model.to(accelerator.device )
						if sched:
												lowerCAmelCase   :List[Any]						=			AdamW(params=model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Dict						=			AdamW(params=ddp_model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Tuple						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
												lowerCAmelCase   :Optional[Any]						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
						# Make a copy of `model`
						if sched:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :int						=			accelerator.prepare(a__     ,      a__     ,      a__     ,      a__ )
						else:
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			accelerator.prepare(a__     ,      a__ )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :Any						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad     ,      ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Any						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Dict						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :str						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :int						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :str						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			get_training_setup(a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Dict						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Any						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Optional[int]						=			ddp_input[torch.randperm(len(a__ ) )]
						GradientState._reset_state()


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			get_training_setup(a__     ,      a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
												lowerCAmelCase   :int						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
												if accelerator.num_processes > 1:
																		check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
						GradientState._reset_state()


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Accelerator()

						lowerCAmelCase   :int						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase   :Any						=			RegressionDataset(length=96 )
						lowerCAmelCase   :Dict						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase	, lowerCAmelCase   :Tuple						=			accelerator.prepare(a__     ,      a__ )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(a__ ):
												assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
												if iteration < len(a__ ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(a__ ):
																														assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
																														if batch_num < len(a__ ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			Accelerator()
						lowerCAmelCase   :Optional[int]						=			accelerator.state
						if state.local_process_index == 0:
												print('**Test `accumulate` gradient accumulation with dataloader break**' )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print('**Test NOOP `no_sync` context manager**' )
												test_noop_sync(a__ )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print('**Test Distributed `no_sync` context manager**' )
												test_distributed_sync(a__ )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    '**Test `accumulate` gradient accumulation, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																								test_gradient_accumulation(a__     ,      a__ )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version('<'     ,      '2.0' ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      '`split_batches=False`, `dispatch_batches=False`**'     ,      )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																														test_gradient_accumulation_with_opt_and_scheduler(a__     ,      a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()